Face recognition app reaches high accuracy by being average

Scientists think human facial recognition may work by using an average …

As we've discussed before, human image recognition can easily outperform even the best software currently available. Humanity's edge over the machines extends to faces, where humans can make accurate identifications from any angle, even as a person's appearance changes with age. In a new paper in Science, researchers report mimicking the process by which they think human brains manage that feat and show that it vastly improved the accuracy of off-the-shelf facial recognition software.

An earlier paper by the authors, who are based at the University of Glasgow, suggested that the human brain may work by averaging many mental images of a single individual to aid in recognition. These averages eliminate the variability in factors like lighting and viewing angle that affect individual images. In practice, they found that software that generated an average face for recognition worked far better than software that matched faces based on the features of individual images.

The new paper flips this technique on its head. Instead of developing custom software, the researchers tested an online version of Cognitec's FaceVACS system, which is used for automated face recognition at airports. The authors chose faces of famous people to ensure the system had several images of the people in question. Starting with a dataset of 500 images, they eliminated those images that were in the FaceVACS database, and then tested the system's accuracy using the remaining 459 images, which represented 25 individuals.

When using individual images, the system's accuracy largely depended on how many images of the person in question were already present in the FaceVACS database. With a low of seven, its accuracy was only 16 percent; when 28 images were stored, accuracy with the query images rose to about 90 percent. The authors then used 20 images from each of these 25 individuals to generate a single, averaged image. When those average images were fed to the system, they found that the accuracy of identification shot up to a low of 55 percent, while the system hit complete accuracy at the high end.

As a second test of the power of average images, the authors selected only those images that the system failed to identify, and generated average faces for each individual from these. Averaging brought the recognition rate up to 80 percent, a dramatic improvement.

The authors suggest that incorporating image averaging into existing databases or identity documents would be relatively easy, and could radically improve the accuracy of automated recognition systems. Given that such systems are already used despite their accuracy issues, anything that makes them more exact would seem to be a major step forward.